# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations
from datetime import timedelta
from functools import cached_property
from typing import TYPE_CHECKING, Any, Sequence
from deprecated import deprecated
from airflow.configuration import conf
from airflow.exceptions import AirflowException
from airflow.providers.amazon.aws.hooks.batch_client import BatchClientHook
from airflow.providers.amazon.aws.triggers.batch import BatchJobTrigger
from airflow.sensors.base import BaseSensorOperator
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class BatchSensor(BaseSensorOperator):
"""
Poll the state of the Batch Job until it reaches a terminal state; fails if the job fails.
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:BatchSensor`
:param job_id: Batch job_id to check the state for
:param aws_conn_id: aws connection to use, defaults to 'aws_default'
:param region_name: aws region name associated with the client
:param deferrable: Run sensor in the deferrable mode.
:param poke_interval: polling period in seconds to check for the status of the job.
:param max_retries: Number of times to poll for job state before
returning the current state.
"""
[docs] template_fields: Sequence[str] = ("job_id",)
[docs] template_ext: Sequence[str] = ()
def __init__(
self,
*,
job_id: str,
aws_conn_id: str = "aws_default",
region_name: str | None = None,
deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False),
poke_interval: float = 5,
max_retries: int = 5,
**kwargs,
):
super().__init__(**kwargs)
self.job_id = job_id
self.aws_conn_id = aws_conn_id
self.region_name = region_name
self.deferrable = deferrable
self.poke_interval = poke_interval
self.max_retries = max_retries
[docs] def poke(self, context: Context) -> bool:
job_description = self.hook.get_job_description(self.job_id)
state = job_description["status"]
if state == BatchClientHook.SUCCESS_STATE:
return True
if state in BatchClientHook.INTERMEDIATE_STATES:
return False
if state == BatchClientHook.FAILURE_STATE:
raise AirflowException(f"Batch sensor failed. AWS Batch job status: {state}")
raise AirflowException(f"Batch sensor failed. Unknown AWS Batch job status: {state}")
[docs] def execute(self, context: Context) -> None:
if not self.deferrable:
super().execute(context=context)
else:
timeout = (
timedelta(seconds=self.max_retries * self.poke_interval + 60)
if self.max_retries
else self.execution_timeout
)
self.defer(
timeout=timeout,
trigger=BatchJobTrigger(
job_id=self.job_id,
aws_conn_id=self.aws_conn_id,
region_name=self.region_name,
waiter_delay=int(self.poke_interval),
waiter_max_attempts=self.max_retries,
),
method_name="execute_complete",
)
[docs] def execute_complete(self, context: Context, event: dict[str, Any]) -> None:
"""
Execute when the trigger fires - returns immediately.
Relies on trigger to throw an exception, otherwise it assumes execution was successful.
"""
if event["status"] != "success":
raise AirflowException(f"Error while running job: {event}")
job_id = event["job_id"]
self.log.info("Batch Job %s complete", job_id)
@deprecated(reason="use `hook` property instead.")
[docs] def get_hook(self) -> BatchClientHook:
"""Create and return a BatchClientHook."""
return self.hook
@cached_property
[docs] def hook(self) -> BatchClientHook:
return BatchClientHook(
aws_conn_id=self.aws_conn_id,
region_name=self.region_name,
)
[docs]class BatchComputeEnvironmentSensor(BaseSensorOperator):
"""
Poll the state of the Batch environment until it reaches a terminal state; fails if the environment fails.
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:BatchComputeEnvironmentSensor`
:param compute_environment: Batch compute environment name
:param aws_conn_id: aws connection to use, defaults to 'aws_default'
:param region_name: aws region name associated with the client
"""
[docs] template_fields: Sequence[str] = ("compute_environment",)
[docs] template_ext: Sequence[str] = ()
def __init__(
self,
compute_environment: str,
aws_conn_id: str = "aws_default",
region_name: str | None = None,
**kwargs,
):
super().__init__(**kwargs)
self.compute_environment = compute_environment
self.aws_conn_id = aws_conn_id
self.region_name = region_name
@cached_property
[docs] def hook(self) -> BatchClientHook:
"""Create and return a BatchClientHook."""
return BatchClientHook(
aws_conn_id=self.aws_conn_id,
region_name=self.region_name,
)
[docs] def poke(self, context: Context) -> bool:
response = self.hook.client.describe_compute_environments( # type: ignore[union-attr]
computeEnvironments=[self.compute_environment]
)
if not response["computeEnvironments"]:
raise AirflowException(f"AWS Batch compute environment {self.compute_environment} not found")
status = response["computeEnvironments"][0]["status"]
if status in BatchClientHook.COMPUTE_ENVIRONMENT_TERMINAL_STATUS:
return True
if status in BatchClientHook.COMPUTE_ENVIRONMENT_INTERMEDIATE_STATUS:
return False
raise AirflowException(
f"AWS Batch compute environment failed. AWS Batch compute environment status: {status}"
)
[docs]class BatchJobQueueSensor(BaseSensorOperator):
"""
Poll the state of the Batch job queue until it reaches a terminal state; fails if the queue fails.
.. seealso::
For more information on how to use this sensor, take a look at the guide:
:ref:`howto/sensor:BatchJobQueueSensor`
:param job_queue: Batch job queue name
:param treat_non_existing_as_deleted: If True, a non-existing Batch job queue is considered as a deleted
queue and as such a valid case.
:param aws_conn_id: aws connection to use, defaults to 'aws_default'
:param region_name: aws region name associated with the client
"""
[docs] template_fields: Sequence[str] = ("job_queue",)
[docs] template_ext: Sequence[str] = ()
def __init__(
self,
job_queue: str,
treat_non_existing_as_deleted: bool = False,
aws_conn_id: str = "aws_default",
region_name: str | None = None,
**kwargs,
):
super().__init__(**kwargs)
self.job_queue = job_queue
self.treat_non_existing_as_deleted = treat_non_existing_as_deleted
self.aws_conn_id = aws_conn_id
self.region_name = region_name
@cached_property
[docs] def hook(self) -> BatchClientHook:
"""Create and return a BatchClientHook."""
return BatchClientHook(
aws_conn_id=self.aws_conn_id,
region_name=self.region_name,
)
[docs] def poke(self, context: Context) -> bool:
response = self.hook.client.describe_job_queues( # type: ignore[union-attr]
jobQueues=[self.job_queue]
)
if not response["jobQueues"]:
if self.treat_non_existing_as_deleted:
return True
else:
raise AirflowException(f"AWS Batch job queue {self.job_queue} not found")
status = response["jobQueues"][0]["status"]
if status in BatchClientHook.JOB_QUEUE_TERMINAL_STATUS:
return True
if status in BatchClientHook.JOB_QUEUE_INTERMEDIATE_STATUS:
return False
raise AirflowException(f"AWS Batch job queue failed. AWS Batch job queue status: {status}")
